linear_algebra

This repository contains fundamental aspects of linear algebra (LA) to understand machine learning (ML) algorithms

Importances aspects of LA in ML:

• The use of linear algebra structures when working with data such as tabular datasets and images.
• Linear algebra concepts when working with data preparation such as one hot encoding and dimensionality reduction.
• The in-grained use of linear algebra notation and methods in subfields such as deep learning, natural language processing and recommender systems.

Some examples of LA in ML are:

1. Dataset and Data Files
2. Images and Photographs
3. One Hot Encoding
4. Linear Regression
5. Regularization
6. Principal Component Analysis
7. Singular-Value Decomposition
8. Latent Semantic Analysis
9. Recommender Systems
10. Deep Learning

Based on: Brownlee, John. (2021) Basics of Linear Algebra for Machine Learning. Machine Learning Mastering.